16
Biotechnology Virtual Labs: Facilitating
Laboratory Access Anytime-Anywhere
for Classroom Education
Shyam Diwakar, Krishnashree Achuthan, Prema Nedungadi and Bipin Nair
Amrita Vishwa Vidyapeetham (Amrita University)
India
1. Introduction
Biotechnology is becoming more popular and well identified as a mainline industry.
Students have shown greater interest in learning the techniques. As a discipline,
biotechnology has led to new advancements in many areas. Criminal investigation has
changed dramatically thanks to DNA fingerprinting. Significant advances in forensic
medicine, anthropology and wildlife management have been noticed in the last few years.
Biotechnology has brought out hundreds of medical diagnostic tests that keep the blood
safe from infectious diseases such as HIV and also aid detection of other conditions early
enough to be successfully treated. Medical kits for diabetes, blood cholesterol and home
pregnancy tests are also biotechnology diagnostic products. Industrial biotech
applications have led to cleaner processes that produce less waste and use less energy and
water in such industrial sectors as chemicals, pulp and paper, textiles, food, energy, and
metals and minerals. Laundry detergents produced in many countries contain
biotechnology-based enzymes making them nature friendly and safer. Agricultural
biotechnology benefits farmers, consumers and the environment by increasing yields and
farm income, decreasing pesticide applications and improving soil and water quality, and
providing healthful foods for consumers. Biotechnology has created more than 200 new
therapies and vaccines, including products to treat cancer, diabetes, HIV/ AIDS and
autoimmune disorders.
This rise in application has led to an increased rise in the number of students undertaking
University-level biotechnology courses. However, biotechnology education requires an
eclectic approach of combining various sub-disciplines. Biology courses and chemistry
courses in biotechnology have diversified the approach of the topic. Most common
courses that biotechnology degree programs focus at the University level in India consist
of cell biology, molecular biology, microbiology, immunology, ecology, statistics and
biophysics.
A brief description of the courses will be sketched so a better picture can be understood
on the university-level curriculum at most places in India and abroad. Cell biology is a
course that focuses on theoretical fundamentals behind the structure, function, and
biosynthesis of cellular membranes and organelles; cell growth and oncogenic
www.intechopen.com
380
Innovations in Biotechnology
transformation; transport, receptors, and cell signaling; the cytoskeleton, the extracellular
matrix, and cell movements; chromatin structure and RNA synthesis. Molecular biology
course covers a detailed analysis of the biochemical mechanisms that control the
maintenance, expression, and evolution of prokaryotic and eukaryotic genomes. The
topics also include gene regulation, DNA replication, genetic recombination, and mRNA
translation. In particular, the logic of experimental design and data analysis is
emphasized. Microbiology course introduces students to the principles of infectious
agents. Fundamental techniques in microbiological researches, such as sterilization,
isolation, morphological observation, and cultivation are usually covered. Immunology
courses focus on the mechanisms which govern the immune response. This will usually
include the cells, organs and molecules that mediate the innate and adaptive aspects of
the immune system as they apply to infection, tumor recognition, autoimmune diseases,
immunodeficiency, cancer and hypersensitivity. Population ecology courses introduce
students to major concepts in population ecology including topics such as mathematical
models of population growth, population viability analysis, habitat fragmentation and
meta-populations, dispersal, population harvesting, predation and population cycles,
competition, and estimation of population parameters in the field. Biochemistry course
explores the roles of essential biological molecules focusing on protein chemistry, while
covering lipids and carbohydrates. It provides a systematic and methodical application of
general and organic chemistry principles. Students examine the structure of proteins, their
function, their binding to other molecules and the methodologies for the purification and
characterization of proteins. Enzymes and their kinetics and mechanisms are covered in
detail. Metabolic pathways are examined from thermodynamic and regulatory
perspectives. A typical course in biochemistry provides the linkage between the inanimate
world of chemistry and the living world of biology. Biophysics is a course that usually
links to the study of underlying physical phenomenon in biology and their function.
Biophysics course usually cover techniques, methods and applications besides molecular
structure and function. Topics in biophysics covered will include an introduction to cell
and molecular biology, biorheology, Brownian motion, molecular interactions in
macromolecules, protein and nucleic acid structure, physics of biopolymers, chemical
kinetics, mechanical and adhesive properties of biomolecules, molecular manipulation
techniques, cell membrane structure, membrane channels and pumps, molecular motors,
neuronal biophysics and related biophysical mechanisms. A very significant yet
seemingly unrelated course is biostatistics. A single introductory course in biostatistics
involves an emphasis on principles of statistical reasoning, underlying assumptions, and
careful interpretation of results. Topics covered include descriptive statistics, graphical
displays of data, introduction to probability, expectations and variance of random
variables, confidence intervals and tests for means, differences of means, proportions,
differences of proportions, chi-square tests for categorical variables, regression and
multiple regressions, an introduction to analysis of variance.
Using software technologies for education has become a new trend. Computer-based
technologies developed by academic institutions as well as industries worldwide are
revolutionizing the educational system. A new field involving the use of virtual reality
techniques is becoming the training environment. Through virtual labs, a new
interdisciplinary field of science brings together biologists and physicists to tackle this grand
challenge through quantitative experiments and models. Using several pro-learning even
www.intechopen.com
Biotechnology Virtual Labs: Facilitating Laboratory
Access Anytime-Anywhere for Classroom Education
381
distance education courses have started using virtual laboratories to enable students to
access equipment since they are independent from opening hours and the work schedule of
the staff. In many engineering courses within India, simulation is the most effective tool in
training students in the use of sophisticated as well as complicated instruments that are
routinely employed in modern biological and chemical laboratories. For the life sciences,
this also circumvents the use of expensive and hazardous biological and chemical agents
which toxic to the experimentalists as well as to the environment. Above all, the virtual lab
technology is cheap as well as cost effective.
Education in many universities and research institutes include their own virtual laboratories
on the web, which are accessible to people around the world. Although some laboratory
practice requires getting one’s hands ‘dirty’, it has already been established that the Virtual
Lab enables the students to understand the underlying principles and the theory behind
laboratory experiments. E-learning plays and will play an important role in diverse regions
such as India where the traditional lab facilities at Universities are not very well localized to
suit requirements of all sub-regions. With multi-campus scenarios as in some Universities
such as ours, offering cross-disciplinary courses needs to exploit the use of extensive elearning facilities (Bijlani et al., 2008).
Biotechnology lab courses richly rely upon new up-to-date content and various techniques
that require a new synergy of knowledge and experimental implementation. Hence a new
kind of experimental science that can be brought as a virtual simulation based laboratory is
necessary. The developments of the virtual labs include mathematical techniques in biology
to study, to hypothesize and to demonstrate complex biological functions. However virtual
labs in heavy engineering topics such as analyzing nanomaterials with high-power
microscopes and lab courses in biotechnology or biology will also have to exploit multiple
techniques besides simulators alone as many scenarios cannot be reproduced
mathematically while retaining the “real” lab-like feel.
In this chapter, we focus on the development and use on the virtual biotechnology
laboratory courses through a combination of techniques to try completing the learning
experience as that of a regular University laboratory.
2. Why virtual labs?
There are many main reasons to focus on creating virtual labs for University education
(Auer et al., 2003). Among the primary reasons include the cost and lack of sufficient skillset for facing the current growth in biotechnology sector. The setup cost of laboratories puts
a large overhead on the educators. The Universities also need to setup laboratories to
educate sufficient target group with the details of common biotechnological techniques and
protocols (O'donoghue et al., 2001).
Another new motivation is the need to introduce and focus well-explored potential virtual
lab areas which use computational methods, mathematical modeling and biophysics,
computational biology and computational neuroscience. Computational biology and
biophysics are upcoming areas and most techniques derive basis from real laboratory
experiments. Another intention of using virtual labs via a computational approach is to
train young scientists in the field of the mathematical thinking for life sciences and related
environments. Main goals of cross-disciplinary sciences include the need to ensure that
www.intechopen.com
382
Innovations in Biotechnology
the students will be able to integrate different exhaustive models into a larger framework,
i.e. in the perspective of comprehensive biological systems such as cells and biological
networks. Such a role will also give an overview of the modeling approaches that are
most appropriate to describe life-science processes. For the everyday biologist, the major
use of virtual labs will also be in the learning perspective of advanced but common-to-use
simulation tools.
Virtual labs and use of virtual tools should lead to an increase the awareness of a crucial
need for standard model descriptions. Most simulators and common-use tools require
various formats and schema and with the explosion of data, the use of virtual labs across the
country or across multiple countries is also intended to unite educators to work towards
common model descriptions and standardization of their data.
For the biotechnology sector, a highly favoring motivation for the shift to the virtual lab
paradigm is the explosion of data-rich information sets, due to the genomics revolution,
which are difficult to understand without the use of analytical tools. Also, recent
development of mathematical tools such as chaos theory to help understand complex,
nonlinear mechanisms in biology seems to push the need for information-rich virtual labs in
simulation domain.
To aid further, an increase in computing power which enables calculations and simulations
to be performed that were not previously possible, have set a new trend in the concept and
use of computing. Simulations in the past that needed more intensive computers now can
plainly be run through long battery-life laptops (Aycock et al., 2008), given that in many
cases laptops today even host servers.
A slightly different reason that also pushes the concept of virtual labs for undergraduate
and master level education at the Universities also seems to be an increasing interest in in
silico experimentation due to ethical considerations, risk, unreliability and other
complications involved in human and animal research.
Given all above reasons and motivation, virtual labs are today’s experimental approach
towards a newer trend in future education. However the virtual lab environments are still
under severe testing and newer models seems to switch to more intelligent and adaptive
platforms that can yield efficient knowledge dissipation. One such common model is the
adaptive learning system (ALS) currently employed by many e-learning applications
strewed on the internet.
3. Other virtual labs and online courses in biosciences
Very little work has been actually done in the biology sector. There are online "dissections" of
frog tutorials by Mable Kinzie developed in 1994 and an improved version of the same was
hosted in 2002 (http://curry.edschool.Virginia.EDU/go/frog/menu.html). Quick "movies":
http://www.bio.unc.edu/faculty/goldstein/lab/movies.html Virtual “experiments”: Biology
Labs On-Line (BLOL) is a collaboration of the California State University system Center for
Distributed Learning and Addison Wesley Longman, with partial funding provided by the
National Science Foundation (http://biologylab.awlonline.com). A project titled "BIOTECH
Project" developed by University of Arizona, with aim of supporting Arizona teachers to
conduct molecular genetics (DNA science) experiments with their students and assists teachers
www.intechopen.com
Biotechnology Virtual Labs: Facilitating Laboratory
Access Anytime-Anywhere for Classroom Education
383
in developing new activities for their classroom (http://biotech.bio5.org/home). "Protein Lab"
by A.J. Booth, is a computer simulation of protein purification. These labs are extremely
helpful for beginners in the art of protein purification. It gives them a chance to get beyond the
details of individual techniques and get a sense of the overall process of a protein purification
strategy. (http://www.booth1.demon.co.uk/archive).To enhance education, there is a great
need for individualized courseware to provide educational content that fits to the learner’s
learning style and knowledge base. University of Utah’s genetic science learning center has its
very animated genetics labs at http://learn.genetics.utah.edu/gslc. The labs were developed
with the mission in making science easy for everyone to understand. Similar projects at
Howard
Hughes
http://www.hhmi.org/biointeractive/vlabs
and
at
Pearson’s
http://www.phschool.com/science/biology_place/labbench have been useful as virtual
education websites.
Online biotechnology courses are available through several leading universities around
the world, including the Massachusetts Institute of Technology (MIT), Osaka University
and the Open University. OpenCourseWare (OCW) from TUFTS and MIT offer courses on
the Web that containing all or some of the materials from the university's original oncampus classrooms. Many biotechnology courses on OCW make use of several different
learning materials available online or by download, lab notes, assignments, lectures on
scientific communications and study materials. Online biotechnology courses are known
to be very helpful for students to study/prepare for the positions as lab technicians,
research assistants and quality assurance analysts in such fields as agriculture,
pharmaceuticals and manufacturing.
4. Amrita VL
Amrita University’s Virtual and Accessible Laboratories Universalizing Education (VALUE)
initiative was initially targeted towards making biotechnology, physics and chemistry
courses virtually accessible for undergraduate and postgraduate education. The project led
to the development of 14 labs in biotechnology and 13 labs in physical and chemical
sciences. The schema of virtual labs was based on one of our studies.
An average survey of the VL framework software was performed and the tests were shown
(see Table 1 in Diwakar et al., 2011). The developed virtual labs are available for public use
(See http://amrita.edu/virtuallabs). Any user may login with an open-id or Google’s gmail
account and access the authentication-compulsory regions such as the remote-panel,
simulator and animations. The website uses the name and email address that provider gives
only to set up an user account.
5. Techniques – Animation, simulations and remote-triggered experiments
The key learning component in many biological laboratories is the complexity of the
procedure and details of the step-by-step protocol carried out in the laboratory. Although
some of these biological processes can be replaced by mathematical equations modeling
the system, most of the “feel” is in performing the detailed procedure which is not
derived from sets of equations. Graphical animations deliver a high degree of the reality
to the virtual labs through their seeming closeness to the appearance and feel of the lab.
Graphical animations also cut out the complexity of the modeling process by increasing
www.intechopen.com
384
Innovations in Biotechnology
the “feel” of experiment. Like the proverb goes, “a picture is worth a thousand words”,
animations reveal better information that cannot be easily conveyed via text alone or static
illustration.
In our biotechnology virtual labs, the animation type of experiments include the use of 2D
flash based animations for illustrating detailed procedures such as wet lab protocols and
heavy engineering techniques that are out of scope for simulation due to various reasons
like complicated equations, numerical issues in simulation, lack of modeling data etc.
Besides animation, another common technique in our virtual labs included engineeringbased approaches such as remote-triggered experiments or remote-controlled
experiments.
The very common and research-inspiring approach is the use of mathematical simulators to
model biological and biotechnological processes or sub-processes. Although mathematics
has long been intertwined with the biological sciences, an explosive synergy between
biology and mathematics seems poised to enrich and extend both fields greatly in the
coming decades. Among the various scenarios to study biology and disseminate
information effectively and efficiently, includes the use of e-learning as a medium to offer
courses.
Applying mathematics to biotechnology for virtual lab creation has recently turned into an
explosion of interest in the field. The NASA virtual laboratory or the HHMI virtual labs at
Howard Hughes Medical Institute or the Utah genetics virtual laboratory are some
examples.
For our labs, a combination of user-interactive animation, mathematical simulations,
remote-trigger of actual equipment and the use of augmented perception haptic devices are
used to deploy effectively the real laboratory feel of a biotech lab online.
6. Models in biology – As virtual labs
Design of simulation labs requires basic mathematical models. Some models that were used
to develop the virtual labs are listed below.
6.1 Neurophysiology and neuronal biophysics
In order to understand neuronal biophysics and simulations on voltage clamp and current
clamp in detail, we modeled a section of excitable neuronal membrane using the HodgkinHuxley equations (Hodgkin and Huxley, 1952) that can be accessed a graphical web-based
simulator. Various experiments using this simulator deal with the several parameters of
Hodgkin-Huxley equations and will model resting and action potentials, voltage and
current clamp, pharmacological effects of drugs that block specific channels etc. This lab
complements some of the exercises in the Virtual Neurophysiology lab.
6.2 Population ecology
As part of population ecology virtual labs, we developed a set of mathematical ecology
models to understand the basic dynamics and behavior of population in various aspects.
Some models include:
www.intechopen.com
Biotechnology Virtual Labs: Facilitating Laboratory
Access Anytime-Anywhere for Classroom Education
•
•
•
•
385
Exponential growth with continuous and discrete rate of growth. If a population has a
constant birth rate through time and is never limited by food or disease, it has what is
known as exponential growth. With exponential growth the birth rate alone controls
how fast (or slow) the population grows. The objectives include the study the growth
pattern of a population if there are no factors to limit its growth, to understand the
various parameters of a population such as per capita rate of increase(r), per capita rate
of birth (b) and per capita rate of death (d) and to understand how these parameters
affect the rate of growth of a population. A case study on tiger population will indicate
the applicability of exponential models as classroom tools.
Leslie matrix is a discrete, age-structured model of population growth that is very
popular in population ecology. It (also called the Leslie Model) is one of the best
known ways to describe the growth of populations (and their projected age
distribution), in which a population is closed to migration and where only one sex,
usually the female, is considered. This is also used to model the changes in a
population of organisms over a period of time. Leslie matrix is generally applied to
populations with annual breeding cycle.
Study of meta-populations using Levin’s model shows a simple model to understand
population changes. Meta population is a population in which individuals are
spatially distributed in a habitat to two or subpopulations. Populations of butterflies
and coral-reef fishes are good examples of metapopulation. A virtual lab using
Levin’s model explains how to understand the basic concepts and dynamics of
metapopulation and population stability with the help of mathematical models. In
addition it is a study on how variations affect the population dynamics and how the
initial number of patches occupied in a system affects the local extinction after a few
years.
Lotka-Volterra Predator Prey interactions (Wangersky, 1978) and logistic growth
functions.
6.3 Biochemistry, cell biology, microbiology, immunology and molecular biology
Simple linear equations were used to understand molecular mass flow in AGE, PAGE
exercises. No differential equations were used in biology oriented virtual labs where the
focus was on the look and feel. In many cases, animation played a major role in these areas
rather than mathematical simulations. As in the case of realistically animating experiments
there are a lot of advantages; although it cannot be considered as a complete replacement of
real labs due to its limitations. One solution was to provide the necessary details of the
instruments we were using for the lab. Per say, if we use cooling centrifuges for an
experiment in the virtual lab, one may not fully show all details corresponding to the
operating methods of the centrifuge. But in the case of a real laboratory the student gets an
opportunity to have a hands-on experience on the equipment while doing the experiment.
Also, many of the experiments require instrumentation facilities. Also instruments from
different companies have slight differences in design and operating mechanisms, which
may not be shown in the virtual labs. Thus even though virtual lab meets the major target, it
shadows the minor details of the experiments. Not all parameters such as changes in
temperature during an experiment especially (where small changes do not matter) may not
be included in the virtual lab for the sake of simplicity. In a real lab, curious students can
www.intechopen.com
386
Innovations in Biotechnology
perform these kinds of interesting experiments but to do the same in virtualized
experiments is difficult.
7. Major challenges
Setting and developing AMRITA virtual labs (see Fig. 2) as a complete learning experience
has not been an easy task. Amongst the major challenges we faced included usage/design
scalability, deliverability efficiency, network connectivity issues, security and speed of
adaptability to incorporate and update changes into existing experiments.
Owing to the scientific domain, biotechnology lends the following challenges to establishing
virtual labs:
•
•
•
•
•
•
The development of analytical solutions in the arena is limited as biological processes
are typically non-linear and are coupled systems of differential equations in various
forms.
The mathematics behind models is hidden by their complexity and appears refined
through simulation platforms.
Most simulation platforms need direct hands-on experience between teachers and
students.
The number of students that can be catered at any given time is restricted.
Besides, such courses also need simultaneous theoretical explanations which may need
classroom-like scenarios with video presentations, white-board and other tools. We
could overcome the issue here using a collaborative suit, AVIEW (Bijlani et al., 2008).
There are not many courses in India developed for this scenario.
In order to address some of these issues and to overcome restrictions, we deployed virtual
lab experiments as web-client based animations or simulators besides remote triggered
experiments. The virtual lab was based on a website that was designed for favorable use
within intranets and internets. However, efficiency depended on the internet bandwidth
and connectivity. Our target was any campus with a download link of 256kbps should
suffice. To retain this compatibility the animations had to be size-delimited. To overcome
the problem, longer experiments had to be sliced to smaller portions, each loading in
sequence. This was possible as we maintained the virtual lab experiments as flash
animations (Adobe, USA). Having labs in flash environments allowed the scalability and
access although flash based action script programming needed additional programmers and
training.
Other e-learning issues such as student-teacher collaboration via chat, video interfacing etc.
were overcome via AVIEW-like environment (Bijlani et al, 2008). The intention of the virtual
labs was also to extend the facility to develop an applied computational laboratory.
8. Methodology
Amongst others, the focus of having and designing virtual labs was also based on John
Keller’s ARCS model of motivation. Design of courses, simulations and models for
computational approaches in biology will be the highlight. A lot of attention was on courses
whose content will be applicable to the existing P.G. programs.
www.intechopen.com
Biotechnology Virtual Labs: Facilitating Laboratory
Access Anytime-Anywhere for Classroom Education
387
Fig. 1. Sakshat Amrita virtual labs. Accessible at http://amrita.edu/virtuallabs
For all biotech virtual labs, we had set the following lab-level objectives as general
guidelines.
•
•
•
•
•
•
•
Virtual labs should be adaptive. An adaptive e-learning system is a system in which
modifies its behavior (the learning process) in response to the changes in the learners
input data and information gathered from various teaching process. It should be able to
incorporate data and user changes as and when possible.
Introduce and focus virtual lab areas in core computational and protocol-based
biotechnological sciences.
To train young scientists in the field of the mathematical thinking for life sciences and
related environments.
To ensure that they will be able to integrate different exhaustive models into a larger
framework, in the perspective of a comprehensive biological systems such as cells and
biological networks.
To give an overview of the modeling approaches most appropriate to describe lifescience processes.
To give a practical introduction to advanced but common-use simulation tools.
To increase the awareness of a crucial need for standard model descriptions.
www.intechopen.com
388
Innovations in Biotechnology
The implementation of animation and simulation based virtual labs was mainly done in
Action Script 3 in Adobe flash in order to bring better definition to 2-D graphics. Action
script allowed flash swf files as output thereby allowing both a better look-and-feel and an
enhanced interactivity with the software. The physics simulator tools worked reasonably
well. We did not use java as a programming medium in our learning tool to make sure we
have complete cross-OS, cross-browser compatibility, to reduce initial loading time and also
to consider support for the commercial operating systems such as Microsoft’s Windows
platform that support flash better than Java plug-in.
We used a new VLCOP platform (Nedungadi et al., 2011) in its full functionality for the virtual
labs. The minor intention was to deploy preliminary platform with a learning environment
and later render the environment adaptive and intelligent as per the user-audience. The main
reason to precursor with such a test was cost-efficiency. Cost-efficiency of e-learning programs
has been increasingly important because some institutions have failed due to the lack of wellthought out financial plans (Wentling et al, 2002; Morgan, 2000).
Virtual Labs use self-assessment based on questionnaire to evaluate user’s experience.
Although not implemented, an advanced form of the lab is being planned to include
teacher’s assessment, peer-assessment and collaborative assessment. Teacher assessment
will actually have a “real” instructor on the deployment site to evaluate the lab
user/student. Peer-assessment will include any student or teacher to assess another.
Collaborative assessment will include both the instructor and the student to perform
assessment on the completion of an experiment.
For our installation and deployment, we focused to reduce internet downtime. A 2004 study
indicated that overall downtime costs companies an average of 3.6% of annual revenue
(internet sources, see www.sentinelbussiness.it) indicating leading causes for downtime
being software failure and human error. Through our studies, we managed to reduce
unnecessary events and maintain downtime to less than 27 minutes for 6 months (not as in
Amrita Learning software, see Table I in Diwakar et al., 2011). However, this could be
because of our lack of full incorporation of the complex adaptive learning system as it was
done for the schools where it was tested. However a test on real-time upgrade to such a
model based on our previous experiences (data not shown) with Amrita learning
(Nedungadi and Raman, 2010) indicated that overall loss of virtual lab in terms of downtime
will be significantly less.
9. Feedback and assessment
Feedback is usually not used as an evaluator but an assessment tool for student quality.
With that in mind, the virtual lab evealuation criterion was focussed on measuring and
estimating the student’s involvement in the particular experiment of a particular lab. A way
to increment the quantity and timing of feedback is to provide enough detail. Through
animation, we have also increased evaluatory criterion and details in the virtual
environments. It was noted that in more than 95 experiments performed by more than 30
people within a particular time-window there were more than 91% of appreciation (further
statistics pending, data not shown) when two experiments, one with detail oriented
interactive animation and other without interaction were delivered to assess the
involvement of the students in terms of their self-assessment.
www.intechopen.com
Biotechnology Virtual Labs: Facilitating Laboratory
Access Anytime-Anywhere for Classroom Education
389
Fig. 2. Neuron simulator. The Neuron simulator lab uses Hodgkin-Huxley equations to study
and analyze the action potential properties. The simulator allows some pharmacological
studies and complements the neurophysiology virtual lab.
10. Case study: Virtual neurophysiology laboratory
Our preliminary studies in the biotech sector were on neurophysiology techniques. The
virtual neurophysiology laboratory provides an opportunity for students to substitute
classroom physiology course with detailed techniques and protocols of a real laboratory.
Besides the material like chemicals, physiology demands extensive knowledge and
experience from the instructor. For example, rat brain slicing protocol which is the first
experiment (in the virtual lab) takes approximately 6-10 hours to complete training and
about 2-3 weeks to train one student in a real laboratory.
With the focus on time (Rohrig et al., 1999) and learning know-how, we adapted the usual lab
experimental protocols as user-interactive animations of the neurophysiology lab experience.
The work involved both animators and programmers. For some experiments such as brain
slice preparation, animations were sufficient whereas for some others such as HodgkinHuxley neuronal model (Hodgkin et al., 1952, see Fig. 3.) for demonstrating behavior of single
neurons, we used Java based simulator. The same simulator was embedded into other
experiments such as voltage clamp protocol and current clamp protocol to allow the student to
see the corresponding behavior as seen in real neurons (Koch, 1999).
www.intechopen.com
390
Innovations in Biotechnology
A new set of experiments developed included the use of electronic resistance-capacitance
(RC) circuits that could be remotely triggered as mimicking the electrical dynamics of a
passive neuronal membrane. Passive neuronal membranes are modeled as RC-circuits with
high resistance and low capacitance (for more details see Koch, 1999). In the simulation lab
that was developed to complement the exercises of the VL, a model detailed study was
added. Some of the main objectives and experiments using a neuron simulator included:
•
•
•
•
•
•
•
•
•
•
•
•
Modeling action potential
Modeling resting potential
Modeling sodium ion channel and its effect on neural signaling
Modeling delayed rectifier potassium channels
Modeling passive membrane properties
Current clamp protocol
Voltage clamp protocol
Understanding pharmacological implications of ionic currents
Capacitive transients using Voltage Clamp
Effect of temperature on neuronal dynamics
Plotting F-vs-I curve
Plotting V-vs-I curve
Also as part of the labs, we follow a particular formatting for each experiment within the
lab. The goal was to allow the student to study the theory, the approach and do a self-test
before actually going into the simulator or the virtual experiment. Covering some
explanations and incorporating the same theory into the actual “lab” part of the
experiment has been one of the primary goals. Each experiment in the labs (especially in
Biotechnology) opens by default with the textual theory, which can also be randomly
accessed by clicking on the icon “theory”.
All the control and experimental parameters are explained in the “manual”. The
instructor and the student are informed on how various parameters change in the
experiment in the very context of the virtual experimental lab procedure. For those
experiments that have both an animation learning component and simulator component,
each of the user controls and the variable parameters are explained. Also included in the
manual is a help that actually explains the usage of radio controls and icons covered by
the experiment. The intention was to evaluate the basic info that once the student
completes the familiarization process by going through the theory and manual sections,
he/she can take a “self-evaluatory” quiz module that chooses to test the student on some
questions based on the theory background of the experiment.
The “simulator” tab actually leads to the experiment workbench. “Protocol for brain slicing”
that is actually a detailed lab process that would take 6-10 weeks for post-master’s student
to learn and about 3-10 hours per procedure. That experiment we have virtualized by means
of an interactive action script based animation. The second neurophysiology experiment
concerns the modeling of a neuronal cell. In this case we have used a Flash based learning
component along with a HH-simulator of a biophysical neuron.
The “assignment” icon is the lab experiment question with which intention the student
performs the experiment. An instructor version of the assignment will include a model
www.intechopen.com
Biotechnology Virtual Labs: Facilitating Laboratory
Access Anytime-Anywhere for Classroom Education
391
solved question or key tips in case of a protocol-like experiment. Additional reading
material and reference information and other details will be found in our “misc info” icon.
Among the various methodologies the lab covers simulation-based, animation-based and
remote-triggered experiments. The simulator was that of a bio-realistic model cell and
was combined with an interactive animation-based learning-tool made using Flash.
Maldarelli et al. (2009) report the advantage of virtual lab demonstration as an effective
lab tool. The remote-triggered experiments were based on real electronic circuits that
mimic the phenomenon observed in neuronal cells. The basic behavior of Resistancecapacitance circuits that can be modified remotely by a user to study and imitate real
neuronal circuits as he/she does in a neuronal biophysics laboratory on a patch of a
neuronal membrane.
Fig. 3. Remote-triggered Experiment. Remote panel is also made with re-configurable panels
and control options. This experiment emulates action potential generation using analog
neurons.
We also tested the virtual lab via a questionnaire-based feedback for overall quality. Among
the major questions, several virtualizations related questions were presented in the
questionnaire. The general developer/designer related questions included in the lab were to
rate the experiment that was most recently completed, extent of control on the interface,
closeness to lab environment and feel, measurement and analysis of data, user-manual
www.intechopen.com
392
Innovations in Biotechnology
quality, adequacy of bibliography and references, results interpretation, whether any clear
information was gained by using the virtual lab, any problems faced, how helpful the lab
was and overall motivation.
Fig. 4. Tiger population study. Using exponential growth patterns to predict tiger
population in India.
11. Taking project tiger to the classroom: A virtual lab case study
Using a virtual lab was not our only objective. We wanted to test a real scenario and see if
the virtual lab could be used a as research tool. Tiger population study uses virtual labs to
take India’s Project tiger to the classroom. Half of the tiger population in the world is in
India. Due to reduction in their population in large numbers, from 1969 onwards the ‘tiger’
was declared as an endangered species (by CITES). Educating about tiger populations is
vital. Typically courses in population ecology deal about population variations. In this
section, we suggest on the applicative use of population ecology simulators as classroom
models to complete the learning experience for a population ecology laboratory course. This
section also reports the analysis, interpretation and some preliminary predictions in
variations of tiger population in India.
Here we used the exponential growth model experiment in population ecology lab 1 (at
http://amrita.edu/virtuallabs). First step is to select an experiment followed by selecting a
www.intechopen.com
Biotechnology Virtual Labs: Facilitating Laboratory
Access Anytime-Anywhere for Classroom Education
393
mathematical model which is described in the experiment from the set of experiments (see
Fig. 4). Some existing data tested indicated the validation of the technique. (Fig 5B).
11.1 Data collection
Statistical data for this study was collected from Project Tiger which includes the tiger
population from 1972 – 2002 of various tiger reserves (see Table 1). And the second data
set was the crime reported for the numbers of tiger that have been killed in past few years
were from WPSI’s Wildlife Crime Database (14.
WPSI's Tiger Poaching Statistics,
http://www.wpsi-india.org/statistics/index.php).
Fig. 5. Virtual Lab model for tiger population study. Note that exponential model was
chosen based on decline in populations. Predictions with other models such as LotkaVolterra and Logistic growth were inappropriate or had errors.
Growth rate has been calculated by using the formula, Growth rate g(t ) =
(t + 1) − t
. where
N(t + 1)
N( t+ 1) is the total number of individuals at t+1, t’ represent the time in years.
www.intechopen.com
394
Innovations in Biotechnology
11.2 On-screen methods
We have used an adaptive growth rate for different periods as shown in Table 1. Simulator’s
viewable window contain three main tabs, 1) Statistics button will show the growth of
population while the simulation is running 2) Data plots button, will Population size Vs
Time, 3) Worksheet button is an implementation of the model in excel.
11.3 Assumptions with tiger populations and growth model
Population ecology models include several assumptions, in order to realistically apply the
model on data. Growth of prey population is exponential in absence of predators;
•
•
•
Tiger population grows/declines exponentially within a short duration (10 years)
The rate of change of tiger population is proportional to its size.
During the process, the environment does not change in favour of one species and the
genetic adaptation is sufficiently slow.
Although the assumptions make it difficult to actually call the simulation ‘realistic’, the
validations showed a realistic trend and hence for this model, a simple exponential growth
simulator was used.
Year
Total
number
of tigers
1972
1979
1984
1989
1993
1995
1997
2002
2007
2012
268
711
1121
1327
1366
1333
1498
1576
-
Total number
of tigers
predicted by
the model
1521
1586
1664
1718
Growth
rate
0.6230
0.3657
0.1552
0.0285
-0.0247
0.1236
0.0409
0.0468
0.0314
Calculated average growth rate was = 0.1545
Table 1. Shows the statistical data for tiger population from 1972 – 2002 and extended
(prediction column) the curve to 2012 using continuous growth model simulator (data
collected from Tiger project India at http://projecttiger.nic.in/populationinstate.asp).
11.4 Results
Taking real data to class rooms have been very difficult with population ecology due to its
high unpredictability and model-related unreliability. However, using a simple growth rate
simulator and using patterns from a short period, the model shows promise.
The simulation showed predictability in the growth of tiger population in India for the years
2003- 2012 by extending the behavioral pattern of tiger population in India for last 30 years
(see Fig.6). Predicted tiger population for the year 2007 was 1664. According to National
www.intechopen.com
Biotechnology Virtual Labs: Facilitating Laboratory
Access Anytime-Anywhere for Classroom Education
395
Tiger Conservation Authority (on 2008), the total tiger population reaches 1,411 (i.e. ranging
between 1,165 and 1,657). The difference in number of tigers from predicted to this statistical
report may be because of some environmental factors, number of tigers that have been killed
in past few years and the census by National Tiger Conservation Authority was only
partially included West Bengal.
11.5 Conclusion
The predicted data (Table.1, third column) for tiger population in India showed standard
deviation of 10% from real data. With some assumptions, it was possible to use simple
models like exponential growth models for studying tiger populations. For a very short
duration (such as in the data shown in Table 1), basic growth show a slowly saturating
exponential and hence data matched the predictions (see Fig. 6). Online population ecology
experiments developed on the basis of mathematical equations could help students to get a
deeper understanding on model dynamics by exploring the parameter space provided by
the model. Also it is always feasible for the user to supply the real data as input and observe
the corresponding dynamics. The possibility to study such experiments has value.
Biotechnology studies often include data collection and such models allow building simple
hypothesis based on the dynamics. This new e-learning environment engaged and
motivated the students to practice and explore the parametric space provided for the
population ecology experiments.
Newer studies for analyzing fish populations and deer populations are being developed as
part of the ongoing process. Such data will be made available as a virtual lab for continued
use and study. We also noticed that the undergraduate and postgraduate students show an
increased attention to details when we trained them on virtual labs instead of plainly
explaining the theory. There was a 23% (metric not shown) improvement in interest to
critically analyze population models among students who were introduced to population
ecology studies directly virtual labs.
12. Cost of virtual labs
In order to estimate the true financial cost of our virtual lab project, we had to include both
project development and delivery and maintenance costs. As indicated by Kruse (see
http://www.e-learningguru.com/articles/art5_2.htm), design of courseware needed more
initial costs than instructor-led learning but delivery and maintenance is affordably cheaper.
We estimate, based on Amrita learning software experience that there will be negligible
costs for maintain web-based experiments. The main post-deployment costs included
administration and maintenance. The administration and maintenance estimates included
tracking of user-behavior, technical support, content updates and technology updates.
Student material development, instructor costs and subject expert costs were included in the
development expenses.
13. Some evaluatory setbacks and associated feedback
What we know from the Virtual Lab studies performed is that user-involvement in
assessment is vital for improving the knowledge-experience for the user. Self-assessment
hints preliminary results but are not comprehensive. Users tend to show implicit behavior
www.intechopen.com
396
Innovations in Biotechnology
patterns indicating favor of the tool rather than the experiment for their choice of vote.
Interactional voting behavior is also dependent on age and other characteristic learner
attitudes. In our studies, younger students mostly at the undergraduate level evaluated
the tool using mid-range scores compared to the varying yet favorably high votes of the
Master’s and Graduate students in the feedback assessment. Although this may need
further testing, we believe that scores from the higher age-experience level indicated
statistically relevant reliability much more than undergraduates (data not shown due to
pending experiments).
Overall, 27 Master's level students who helped in intensive evaluation of the Virtual lab
platform as part of their regular class-room course, appeared predominantly positive
about the value of virtual labs in e-learning, but anxieties were also expressed about the
potential for e-labs to replace face-to-face teaching and labs in the economically
challenged regions of the country apart an indication either of the value on the personal
and face-to-face tutoring through an expressed preference for it. Students who were
positive about their experiences of virtual labs indicated that they had received
appropriate introductions and felt supported by staff, indicating the importance of sound
inductions into the use of institutional systems and technologies.
Fig. 6. Growth of tigers with predictions. A. The plot shows the nature / pattern of statistical
data for tiger population in India from 1972 – 2002 (blue line) and an extended prediction
(red line) of the curve to 2012 using continuous growth model simulator. The model
assumes a 10% standard deviation shown by the error bar. B. The plot shows the enlarged
curve of predicted piece-wise continuous growth of tiger populations in India
14. Conclusion and further remarks
Education using VL has been the new venture to better education and provide extensible
laboratory experience to University students. The virtual lab protocols for neurophysiology
and related sciences have been a successful complement to the usual theoretical education
that happens at our school of biotechnology at the level of masters and undergraduate
education. Although the elements can be improved, our approach to virtualization has
answered many key results in establishing the virtual lab features such as teacherindependent/teacher-friendly approach to e-learning.
www.intechopen.com
Biotechnology Virtual Labs: Facilitating Laboratory
Access Anytime-Anywhere for Classroom Education
397
We tried to avoid the most usual failures in e-learning labs (Romiszowski, 2004) by
focusing to avoid the common failures. Our design issues were based on a successfully
tested e-learning software environment (Nedungadi and Raman, 2010) and included a
clear identification and analysis of the real problem associated with University laboratory
courses. Each virtual lab included overall strategic design decision such as structure of the
courses, technologies employed and mode of experiment. Each experiment and the lab
was considered with instructional design and elements that were evaluated so to motivate
the learner experience. Such elements included the choice of graphical front-ends and
authoring tools. We had also previously estimated issues related to dissemination for
rapid, efficient and cost-effective usability taking into consideration both pedagogical and
infrastructural complicacy.
Large scale tests will be needed to analyse and provide the assessment. These tests will also
require both learners and educators (lab faculty) to use the software platform. Some tests in
biotechnology are already underway via the VALUE initiative (Diwakar et al. 2011).
Several users raised the issue of how to support learners using VL. In real-world labs,
learners work in the same place at the same time so there is teacher or peer support
available. This kind of support is not immediately available to remote learners.
From our experience, the most vital requirement for each virtual lab is that of technical
coordinators and subject matter experts whose inputs improve the lab’s knowledge bank
and usability. The Virtual lab project is already online for public preview via
http://amrita.edu/virtuallabs or the National mission site http://vlab.co.in
15. Acknowledgment
This project derives direction and ideas from the Chancellor of Amrita University, Sri Mata
Amritanandamayi Devi. The authors would also like to acknowledge the contributions of
Mr. Raghu Raman, Director, CREATE, Amrita University and CREATE team. This project is
funded by NMEICT, MHRD, Government of India.
16. References
Auer M., Pester A., Ursutiu, D., Samoila C., Distributed virtual and remote labs in
engineering, IEEE International Conference on Industrial Technology, Vol. 2, 2003,
pp. 1208-1213.
Aycock J., Crawford H., deGraaf R., Innovation and technology in computer science
education (Proceedings of the 13th Annual Conference on Innovation and
Technology in Computer Science Education, Madrid(Spain), 2008, pp. 142-147.
Bijlani K., Manoj P., Rangan V., VIEW: A Framework for Interactive eLearning in a Virtual
World, Proceedings of the Workshop on E-Learning for Business Needs 2008/BIS,
Innsbruck(Austria),2008, pp. 177-187.
Hodgkin A.L., Huxley A.F., A quantitative description of membrane current and its
application to conduction and excitation in nerve. Bull Math Biol, 1990, Vol 52, pp.
25-71.
O'donoghue J., Singh G., Dorward L., Virtual education in universities: A technological
imperative, British Journal of Educational Psychology, 32(5), 2001, pp.511-523.
www.intechopen.com
398
Innovations in Biotechnology
Rohrig C., Jochheim A., The Virtual Lab for controlling real experiments via Internet,
Proceedings of the 1999 IEEE International Symposium on Computer Aided
Control System Design, 1999, pp. 279 – 284.
Nedungadi P and Raman R. Effectiveness of Adaptive Learning with Interactive Animations
and Simulations. Proceddings of the 2nd International Conference on Computer
Engineering and Applications (ICCEA 2010), Bali, Indonesia, March 2010.
Wentling T and Park J. Cost Analysis of E-learning: A Case Study of A University Program,
Proceedings of the AHRD, University of Illinois at Urbana-Champaign, p.1-11,
2002.
Morgan BM. Is distance learning worth it? Helping to determine the costs of online courses.
Eric number 446611, 2000.
Maldarelli GA, Hartmann EM, Cummings PJ, Horner RD, Obom KM, Shingles R and
Pearlman RS. Journal of microbiology & biology education, pp. 51-57, May 2009.
Romiszowski A. How’s the E-learning Baby? Factors Leading to Success or Failure of an
Educational Technology Innovation Educational Technology, 44(1), Jan-Feb 5-27,
2004.
Koch, C. Biophysics of Computation: Information Processing in Single Neurons,Stryker, M.
(ed.) Oxford University Press, 1999.
Wenger E. Artificial Intelligence and Tutoring Systems: Computational and Cognitive
approaches to the communication of knowledge. Morgan Kaufman Ed., 1987.
Diwakar S, Achuthan K, Nedungadi P and Nair B. Enhanced Facilitation of Biotechnology
Education in Developing Nations via Virtual Labs: Analysis, Implementation and
Case-studies, International Journal of Computer Theory and Engineering vol. 3, no.
1, pp. 1-8, 2011.
Nedungadi P, Raman R, Achuthan K, Diwakar S. Collaborative & Accessibility Platform for
Distributed Virtual Labs, Proceedings of 2011 IAJC-ASEE International Conference,
University of Hartford, NY, USA, April 29-30, 2011.
Wangersky P. Lotka-Volterra poulation models, Ann. Rev. Ecol Systems, 1978, 9:189-218.
www.intechopen.com
Innovations in Biotechnology
Edited by Dr. Eddy C. Agbo
ISBN 978-953-51-0096-6
Hard cover, 474 pages
Publisher InTech
Published online 17, February, 2012
Published in print edition February, 2012
Innovations in Biotechnology provides an authoritative crystallization of some of the evolving leading-edge
biomedical research topics and developments in the field of biotechnology. It is aptly written to integrate
emerging basic research topics with their biotechnology applications. It also challenges the reader to
appreciate the role of biotechnology in society, addressing clear questions relating to biotech policy and ethics
in the context of the research advances. In an era of interdisciplinary collaboration, the book serves an
excellent indepth text for a broad range of readers ranging from social scientists to students, researchers and
policy makers. Every topic weaves back to the same bottom line: how does this discovery impact society in a
positive way?
How to reference
In order to correctly reference this scholarly work, feel free to copy and paste the following:
Shyam Diwakar, Krishnashree Achuthan, Prema Nedungadi and Bipin Nair (2012). Biotechnology Virtual Labs:
Facilitating Laboratory Access Anytime-Anywhere for Classroom Education, Innovations in Biotechnology, Dr.
Eddy C. Agbo (Ed.), ISBN: 978-953-51-0096-6, InTech, Available from:
http://www.intechopen.com/books/innovations-in-biotechnology/biotechnology-virtual-labs-facilitatinglaboratory-access-anytime-anywhere-for-classroom-education
InTech Europe
University Campus STeP Ri
Slavka Krautzeka 83/A
51000 Rijeka, Croatia
Phone: +385 (51) 770 447
Fax: +385 (51) 686 166
www.intechopen.com
InTech China
Unit 405, Office Block, Hotel Equatorial Shanghai
No.65, Yan An Road (West), Shanghai, 200040, China
Phone: +86-21-62489820
Fax: +86-21-62489821